1. Field of the Invention
The present invention relates to a technique of non-invasively estimating a concentration of an in-vivo component such as blood sugar level (glucose).
2. Disclosure of the Prior Art
For health controls and medical treatments, attention has been given to a method of non-invasively analyzing an in-vivo component such as glucose, protein, lipid, water or urea without blood drawing. When using near-infrared light in this analyzing method, there are advantages that an aqueous solution can be analyzed because an absorption spectrum of water in the near-infrared region is small, and also the near-infrared light is easy to propagate in the living body. On the contrary, since a signal level in the near-infrared region is much smaller than the signal level in a mid-infrared region, and also an absorption signal of the target in-vivo component such as glucose is susceptible to concentration changes of other in-vivo components such as water, lipid and protein, it was difficult to accurately analyze the target in-vivo component by using the peak position or the peak height.
In recent years, to improve these inconveniences in the near-infrared spectroscopic analysis, it has been proposed to use a multivariate analysis such as PLS regression analysis. In this case, even when an absorption signal in the near-infrared region is of a poor S/N ratio, or concentration changes of the other in-vivo components occur, a practical quantitative analysis using the near-infrared light becomes possible.
For example, U.S. Pat. No. 5,957,841 discloses a method of determining a glucose concentration in a target by using near-infrared spectroscopy. In this method, a near-infrared radiation is projected on a skin of a subject, and then a resultant radiation from the skin is received by an optical fiber bundle. A spectrum analysis of the received radiation is performed to detect absorption signals from a first wavelength region (e.g., 1550 to 1650 nm) having an absorption peak of OH group derived from glucose molecule, second wavelength region (e.g., 1480 to 1550 nm) having an absorption peak of NH group, and a third wavelength region (e.g., 1650 to 1880 nm) having an absorption peak of CH group. The glucose concentration is determined by multivariate analysis with use of the absorption signals as explanatory variables.
In addition, Japanese Patent Early Publication [kokai] No. 2003-50200 discloses a method of determining the concentration of a target component in a medium according to a stochastic simulation. In this method, light paths in the medium are analyzed by the stochastic simulation such as Monte Carlo method. In addition, a dada table is prepared, which presents a change in diffuse reflectance in the case of changing absorption coefficient and reduced scattering coefficient as optical characteristics of the medium in required ranges, and then a smoothing treatment for the diffuse reflectance is performed by means of a regression analysis to prepare a compensated data table. Next, an actually measured spectrum is obtained by irradiating light such as near-infrared light in a wavelength region of 1000 to 2500 nm to the medium, and detecting a resultant radiation therefrom, and compared with a reference spectrum provided from the compensated data table to determine the concentration of the target component in the medium. In addition, it is disclosed that when calculating a spectrum change caused by a concentration change of a component other than the target component in the medium from the compensated data table, the target component can be determined from the actually measured spectrum by the multivariate analysis such as multiple linear regression (MLR) analysis or principal component regression (PCR) analysis.
However, it is known that a skin of the living body, to which the near-infrared light is irradiated, usually has a nonuniform structure, and there are differences among individuals in thickness of the skin and the skin structure. In addition, the concentration of the target in-vivo component of a subject measured in the morning of a day is often different from the concentration of the subject measured in the evening of the same day. Thus, concentration fluctuations within one day of the target in-vivo component and another in-vivo components having an influence on the concentration of the target in-vivo component of the subject lead to reduction in estimation accuracy of the target in-vivo component.
FIG. 5A shows measurement results of the relation between blood sugar level as the target in-vivo component and the concentration of the other in-vivo component with respect to each of three subjects (A, B, C). In this figure, ellipses (A1, A2, A3, A4) present the measurement results of the subject A in different days. Similarly, ellipses (B1, B2, B3) present the measurement results of the subject B in different days, and ellipses (C1, C2, C3) present the measurement results of the subject C in different days. In addition, plots “m” in each of the ellipses show fluctuations within one day of the measurement values of the respective subject. In this case, since there is a data poor region G, as shown by a dotted line in FIG. 5A, it is difficult to obtain high estimation accuracy at the region G by a calibration curve prepared from the measurement results of FIG. 5A.
Therefore, to provide stable reliability of the estimation accuracy of the target in-vivo component, it is desired to prepare the calibration curve by use of larger amounts of data. However, it leads to a considerable increase in time required for data collection. Additionally, when glucose, i.e., blood sugar level is selected as the target in-vivo component, an absorption signal of glucose is very weak. Therefore, even when the data amounts used are increased, there is a fear that a sufficient improvement of the estimation accuracy is not achieved by the influence of noise components.